https://github.com/matsuolab/BREMEN
공식 코드Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Papers with Code 아카이브
Reinforcement Learning (RL)
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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/matsuolab/uniform-pac-primal-dual-cmdp
공식 코드A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees
https://github.com/matsuolab/variance-weighted-mdvi
공식 코드https://github.com/matthewwicker/BNNIterativePrediction
공식 코드Certification of Iterative Predictions in Bayesian Neural Networks
https://github.com/matthias-mayr/behavior-tree-policy-learning
공식 코드Learning of Parameters in Behavior Trees for Movement Skills
https://github.com/matthias-weissenbacher/Quadratic_MLPs_in_RL
공식 코드A Quadratic Actor Network for Model-Free Reinforcement Learning
https://github.com/matthias-weissenbacher/SiT
공식 코드SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning
https://github.com/matthieu637/DFRL
공식 코드Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
https://github.com/mattolson93/counterfactual-state-explanations
공식 코드Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
https://github.com/mavridischristos/onlinedeterministicannealing
공식 코드Annealing Optimization for Progressive Learning with Stochastic Approximation
https://github.com/mawbray/Lutein-Dataset
공식 코드Safe Chance Constrained Reinforcement Learning for Batch Process Control
https://github.com/maximecb/gym-minigrid
공식 코드The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
https://github.com/maximecb/gym-miniworld
공식 코드Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
https://github.com/maximilianb2/pc-gym
공식 코드https://github.com/maximilianigl/DVRL
공식 코드https://github.com/mazpie/contrastive-aif
공식 코드https://github.com/mazpie/genrl
공식 코드GenRL: Multimodal-foundation world models for generalization in embodied agents
https://github.com/mazpie/mastering-urlb
공식 코드Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
https://github.com/mbar0075/sarlvision
공식 코드Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object Detection
https://github.com/mbhenaff/neural-e3
공식 코드Explicit Explore-Exploit Algorithms in Continuous State Spaces
https://github.com/mcao516/entfa
공식 코드Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
https://github.com/mcao516/rectification-lm
공식 코드Systematic Rectification of Language Models via Dead-end Analysis
https://github.com/mcgill-nlp/vineppo
공식 코드VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment
https://github.com/mcgillmrl/robot_learning
공식 코드Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
https://github.com/mchancan/citylearn
공식 코드MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation
https://github.com/mchldann/SpectralDQN
공식 코드Adapting to Reward Progressivity via Spectral Reinforcement Learning
https://github.com/mcmachado/b-pro
공식 코드State of the Art Control of Atari Games Using Shallow Reinforcement Learning
https://github.com/mcmachado/count_based_exploration_sr
공식 코드https://github.com/mcmachado/options
공식 코드A Laplacian Framework for Option Discovery in Reinforcement Learning
https://github.com/mcogswell/evolang
공식 코드Emergence of Compositional Language with Deep Generational Transmission
https://github.com/mechatronics-rwth/lexci-2
공식 코드LExCI: A Framework for Reinforcement Learning with Embedded Systems
https://github.com/med-air/dex
공식 코드https://github.com/med-air/surrol
공식 코드https://github.com/mees/vapo
공식 코드Affordance Learning from Play for Sample-Efficient Policy Learning
https://github.com/mehooz/vision4leg
공식 코드https://github.com/mehrab-abrar/Sim2Real
공식 코드An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a Grid
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.